Electric vehicles Report

Autonomous cars and robotaxi services will become a $2.5 trillion market by 2040

Autonomous Cars and Robotaxis 2020-2040: Players, Technologies and Market Forecast

Peak car, robotaxi, mobility-as-a-service (MaaS), lidar, radar, camera, HD map, AI software, cybersecurity, 5G and V2X


製品情報概要目次価格
The arrival of autonomous driving will revolutionise the way people travel. Autonomous cars could liberate people from the driving tasks and potentially enhance road safety and efficiency. Autonomous driving (AD) will also improve travel convenience for those who are unable to drive. Currently the autonomous driving system cost is still very high but with the growing maturity of key technologies such as lidars, radars, cameras, artificial intelligence (AI) software and specialized computers, it is expected the cost of autonomous cars will drop significantly in the coming decade.
 
Mobility services enabled by autonomous driving technology, which allows fleet operators to eliminate the biggest operation cost - the human driver - will offer a cheaper alternative to purchasing and owning a private car. In the next two decades, we expect mobility-as-a-service (MaaS) will grow rapidly to meet the increasing travel demand and in the meanwhile gradually replace private driving. IDTechEx forecasts that in a moderate scenario, 30% of the total travel demand will be provided by MaaS by 2040 and global passenger car sales are expected to peak in 2031.
 
Autonomous driving is changing the existing automotive supply chain from the traditional system of OEMs and suppliers to a collaborative ecosystem comprising OEMs, mobility service providers, software and hardware solution providers, as well as infrastructure providers. We have recently seen competitors joining hands and forming some unlikely-sounding alliances to reduce the cost of AD development, as well as to share resources and capabilities.
 
Autonomous driving provides huge value opportunities for a wide range of stakeholders across the mobility sector. This report offers an in-depth analysis of key enabling technologies including lidars, radars, cameras, AI software, HD maps, and 5G & V2X. Key players with their latest technologies and product commercialisation plans are presented as the case studies of this report.
 
We provide a twenty-year market forecast, in both sales numbers and revenues, for both private-owned autonomous cars and shared cars for mobility services (or robotaxis). For the market value forecast, we break it down into the revenues from AV sales as well as revenues from AV mobility services. We built a twenty-year model because our forecast suggests that the transformation towards fully autonomous driving will take place over a long timescale. We also builds a twenty-year market value forecast for the key components of AV systems - lidars, radars, cameras, AI software, computers etc. According to our forecasts, by 2040 global autonomous car (SAE Level 3+) and robotaxi services will become a $2.5 trillion market. By 2030, the autonomous driving system (including lidars, radars, cameras, computers, software and maps) market will reach $57 billion; the market value will more than triple by 2040, reaching $173 billion.
 
 
Market forecast of global autonomous cars and mobility services (left);
Market forecast of autonomous driving system by components (right)
Source: IDTechEx
 
Key issues addressed in this report:
• Introduction to autonomous driving and the passenger car market landscape
• Who are the key players in the autonomous car ecosystem? What are the progresses in terms of AD development so far and what are their commercialisation plans?
• Key enabling technologies for autonomous cars: in-depth and comprehensive analysis of technology trends of lidar, radar, camera, AI software and computing platform, HD maps, cybersecurity, teleoperation, 5G and V2X
• How MaaS and AD technologies will shape the future travel landscape? And how AD-enabled MaaS will impact the passenger car market?
• Twenty-year market forecast for autonomous cars in both unit numbers and market value
 
Technology assessment
This report provides a comprehensive view of all the enabling constituent technologies. In terms of radars, the report develops a comprehensive technology roadmap, examining the technology at the levels of materials, semiconductor technologies, packaging techniques, antenna array, and signal processing. It demonstrates how radar technology can evolve towards becoming a 4D imaging radar capable of providing a dense 4D point cloud that can enable object detection, classification, and tracking AI.
 
In terms of lidars, IDTechEx identified and analysed more than 100 players developing 3D lidars. This report examines all the technology options for the measurement process, light source, photodetector, and beam steering mechanisms. In case of the latter, it examines mechanical, MEMS/MOEMS, optical phase arrays, liquid crystal, 3D flash, and other technologies. The report examines the key players, categorising them by technology, investment, and geography. The report provides market share projections by lidar technology as well as price evolutions within the next decade.
 
In terms of cameras, the report first focuses on trends in global shutter (GS) CMOS image sensors. Here, we consider the key technology performance levels, pixel architectures, and latest innovations including back-side illuminated GS-CIS and organic and quantum dot hybrid GS-CIS. The report also examines means of boosting the NIR sensitivity of CMOS sensors. Finally, the report outlines and analyses existing and emerging technology options for SWIR sensing such as InGaAs, silicon (IPE process), quantum dots, and organics.
 
The report further examines non-hardware elements of autonomous driving. AI, the brain for autonomous cars, has been the major focus of autonomous driving efforts. Deep learning, which mimics neuron activity, supports functions like object recognition and classification, semantic segmentation, path planning in dynamic environments, and complex decision making and execution. Working together, these functions help vehicles understand its surroundings and make the right decisions. In this report, we provide an overview of different AI approaches for autonomous driving.
 
We also review the trends in HD mapping technology. Here, we discuss the progression of maps from ADAS map towards HD maps with many localization layers. We outline the key players and highlight the differences in approach towards collection, labelling and analyses of the data. In terms of teleoperation and cybersecurity, the report identities and overviews many of the key players worldwide. In the future, infrastructure could play a key role in accelerating the deployment of autonomous driving. This report examines how 5G enabled V2X technologies could support safer and more efficient autonomous driving systems.
Analyst access from IDTechEx
All report purchases include up to 30 minutes telephone time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.
Further information
If you have any questions about this report, please do not hesitate to contact our report team at research@IDTechEx.com or call one of our sales managers:

AMERICAS (USA): +1 617 577 7890
ASIA (Japan): +81 90 1704 1184
EUROPE (UK) +44 1223 812300
Table of Contents
1.EXECUTIVE SUMMARY AND CONCLUSIONS
1.1.Why autonomous cars
1.2.Levels of automation in cars
1.3.Future mobility scenarios: autonomous and shared
1.4.Travel demand and mobility as a service (MaaS)
1.5.Passenger car sales will peak earlier than expected
1.6.Autonomous driving testing race in California
1.7.Roadmap of autonomous driving functions
1.8.Overview of announced autonomous car launch time
1.9.The race for robotaxis
1.10.Autonomous driving is changing the automotive supply chain
1.11.Lidar cost will drop significantly
1.12.Global Lidar market size by technology
1.13.The evolving role of the automotive radar towards full 360degree imaging
1.14.Radar market forecasts 2020-2040 in all levels of autonomy/ADAS in vehicles and trucks (market value)
1.15.SWIR: incumbent and emerging technology options
1.16.Many layers of an HD map for autonomous driving
1.17.Application of AI to autonomous driving
1.18.There is no single AI solution to autonomous driving
1.19.Cybersecurity risks for autonomous cars
1.20.Why Vehicle-to-everything (V2X) is important for future autonomous vehicles
1.21.Use cases of 5G NR C-V2X for autonomous driving
1.22.Global autonomous passenger car sales forecast 2020-2040
1.23.Global private autonomous car sales forecast 2020-2040
1.24.Global shared autonomous car sales forecast 2020-2040
1.25.Global autonomous passenger cars market forecast 2020-2040 - summary
1.26.Global autonomous car market forecast 2020-2040: AV sales and mobility service (market value)
1.27.Global autonomous car sales revenue forecast 2020-2040 by level of autonomy
1.28.Global autonomous car market forecast 2020-2040 (market value: $ million) - summary
1.29.Global autonomous car sales revenue breakdown 2020-2040 (market value)
1.30.Global autonomous car sales revenue breakdown 2020-2040 (market value) - summary
2.INTRODUCTION
2.1.Why autonomous cars
2.2.Challenges to traditional OEMs
2.3.Future mobility scenarios: autonomous and shared
2.4.Product and value positioning of autonomous cars
2.5.OEMs are becoming mobility service providers
2.6.What are the levels of automation in cars
2.7.The automation levels in details
2.8.Functions of autonomous driving at different levels
2.9.Roadmap of autonomous driving functions
2.10.Chess pieces: autonomous driving tasks
2.11.Typical toolkit for autonomous cars
2.12.Anatomy of an autonomous car
2.13.Evolution of sensor suite from Level 1 to Level 5
2.14.Two development paths towards autonomous driving
2.15.Autonomous driving is changing the automotive supply chain
2.16.Auto OEMs' partnerships in autonomous driving
3.AUTONOMOUS PASSENGER CARS: KEY PLAYERS AND CASE STUDIES
3.1.Overview of autonomous car launch time by OEMs
3.2.AV testing distance in California by companies
3.3.Waymo leading the game in terms of disengagement rate
3.4.AV testing by auto OEMs in 2018
3.5.AV testing in Beijing, China
3.6.AV development in China: from testing to pilot services
3.7.Volkswagen investing in autonomous driving
3.8.The world's first 'L3 autonomous car'?
3.9.Audi A8 autonomous sensor suite
3.10.Daimler-Bosch joint force in autonomous services
3.11.The world's first fully autonomous parking
3.12.BMW's strategy towards autonomous driving (1)
3.13.BMW's strategy towards autonomous driving (2)
3.14.BMW partners with Daimler for Level 4 autonomation
3.15.Ford 'Autonomous 2021'
3.16.Toyota's investment in autonomous driving
3.17.Toyota's dual approach to autonomy
3.18.e-Palette - Toyota's multipurpose mobility platform
3.19.Nissan-Renault-Mitsubishi roadmap to autonomy
3.20.Renault's Eyes-off/Hands-off system
3.21.Honda catching up in the autonomy race
3.22.Hyundai's strategic partnerships on autonomous driving
3.23.Hyundai-Aptiv joint venture for L4+ autonomous driving
3.24.Volvo betting on highly autonomous driving
3.25.PSA's Autonomous Vehicle for All program
3.26.BYD's open-source AV technology platform
3.27.Geely's four-step plan for autonomous driving
3.28.Changan is testing AVs on the 5G-V2X platform
4.ROBOTAXI: AUTONOMOUS MOBILITY AS A SERVICE
4.1.OEMs are becoming mobility service providers
4.2.Mobility services launched by auto OEMs
4.3.Mobility service cost: autonomous vs non-autonomous
4.4.Overview of robotaxi launch time announced by AV companies
4.5.Waymo started semi-commercial robotaxi service
4.6.Waymo Driver's sensor architecture
4.7.Waymo's strategic partnerships
4.8.GM is betting on autonomous cars through Cruise
4.9.Cruise sensor architecture
4.10.Uber is building autonomous car on Volvo platform
4.11.Change in sensor suite in Uber's autonomous cars
4.12.Zoox is developing self-driving cars from scratch
4.13.Will Tesla robotaxi hit the road in 2020?
4.14.Tesla autopilot sensor suite
4.15.Chinese Pony.ai is testing robotaxi in China and US
4.16.Pony.ai's sensor fusion
4.17.Chinese AutoX launched the first robotaxi service in CA
4.18.AutoX is expanding its robotaxi business
4.19.AutoX sensor set
4.20.Baidu robotaxi service: Apollo Go
4.21.DiDi's plan to launch robotaxi services
4.22.WeRide plans to launch robotaxi in China by 2020
4.23.Aptiv aims to achieve fully driverless by 2020
4.24.Yandex launched robotaxi service in Russia
4.25.Renault and Nissan preparing for robotaxi service
4.26.Voyage targeting on robotaxi in retirement community
4.27.Sensor configuration of Voyage autonomous car
4.28.Voyage's strategic partnership with Enterprise
5.ENABLING TECHNOLOGIES: LIDARS, RADARS, CAMERAS, AI SOFTWARE AND COMPUTING PLATFORM, HD MAP, TELEOPERATION, CYBERSECURITY, 5G AND V2X
5.1.Overview
5.1.1.Chess pieces: autonomous driving tasks
5.1.2.Typical toolkit for autonomous cars
5.1.3.Anatomy of an autonomous car
5.1.4.Evolution of sensor suite from Level 1 to Level 5
5.1.5.What is sensor fusion?
5.1.6.Sensor fusion: past and future
5.2.Lidars
5.2.1.3D Lidar: market segments & applications
5.2.2.3D Lidar: four important technology choices
5.2.3.Comparison of Lidar, Radar, Camera & Ultrasonic sensors
5.2.4.Automotive Lidar: SWOT analysis
5.2.5.Automotive Lidar: operating process & requirements
5.2.6.Emerging technology trends
5.2.7.Comparison of TOF & FMCW Lidar
5.2.8.Laser technology choices
5.2.9.Comparison of common Laser type & wavelength options
5.2.10.Beam steering technology choices
5.2.11.Comparison of common beam steering options
5.2.12.Photodetector technology choices
5.2.13.Comparison of common photodetectors & materials
5.2.14.106 Lidar players by geography
5.2.15.Lidar hardware supply chain for L3+ vehicles
5.2.16.Beam steering technology
5.2.17.Mechanical Lidar players, rotating & non-rotating
5.2.18.Micromechanical Lidar players, MEMS & other
5.2.19.Pure solid-state Lidar players, OPA & liquid crystal
5.2.20.Pure solid-state Lidar players, 3D flash
5.2.21.Players by technology & funding secured
5.2.22.Lidars per vehicle by technology & common fonfigurations
5.2.23.Lidar configuration diagrams: L3, L4 & L5 vehicles
5.2.24.Average Lidar cost per vehicle by technology
5.2.25.L3 private vehicle market share by Lidar technology
5.2.26.L4 & L5 private vehicle market share by Lidar technology
5.2.27.L4 & L5 shared mobility market share by Lidar technology
5.2.28.Global Lidar unit sales by L3+ vehicle type
5.2.29.Global Lidar market size by L3+ vehicle type
5.2.30.Global Lidar unit sales by technology
5.2.31.Global Lidar market size by technology
5.3.Radars
5.3.1.Towards ADAS and autonomous driving: increasing sensor content
5.3.2.Towards ADAS and autonomous driving: increasing radar use
5.3.3.SRR, MRR and LRR: different functions
5.3.4.The evolving role of the automotive radar towards full 360degree imaging
5.3.5.Automotive radars: role of legislation in driving the market
5.3.6.Automotive radars: frequency trends
5.3.7.Automotive radars: frequency trends
5.3.8.Automotive radars: frequency trends
5.3.9.Radar: which parameters limit the achievable KPIs
5.3.10.Impact of frequency and bandwidth on angular resolution
5.3.11.Why are radars essential to ADAS and autonomy?
5.3.12.Towards autonomy: Increasing semiconductor use
5.3.13.Performance levels of existing automotive radars
5.3.14.Radar players and market share
5.3.15.Radar market forecasts 2020-2040 in all levels of autonomy/ADAS in vehicles and trucks (unit numbers)
5.3.16.Radar market forecasts 2020-2040 in all levels of autonomy/ADAS in vehicles and trucks (market value)
5.3.17.Radar market forecasts 2020-2040 in all levels of autonomy/ ADAS in vehicles and trucks (market value)- moderate
5.3.18.Radar market forecasts 2020-2040 in all levels of autonomy/ ADAS in vehicles and trucks (market value)- aggressive
5.3.19.Radar semiconductor market share forecast (GaAs, SiGe, Si)
5.3.20.Ten year (unit number) market forecasts for automotive radars
5.3.21.Benchmarking of semiconductor technologies for mmwave radars
5.3.22.The choice of the semiconductor technology
5.3.23.The choice of the semiconductor technology
5.3.24.SiGe: current and emerging performance levels
5.3.25.SiGe: current and emerging performance levels
5.3.26.SiGe: overview and comparison of manufacturers
5.3.27.SiGe BiCMOS: Infineon Technology
5.3.28.SiGe BiCMOS: NXP
5.3.29.SiGe BiCMOS: ST Microelectronics
5.3.30.A closer look at SiGe vs Si CMOS
5.3.31.A closer look at SiGe vs Si CMOS
5.3.32.Emerging all Si CMOS radar IC packages: NXP
5.3.33.Emerging all Si CMOS radar IC packages: ADI
5.3.34.Emerging all Si CMOS radar IC packages: TI
5.3.35.Many chip makers are on-board
5.3.36.Packaging trends: from discreet bare die (COB) to wafer-level packaging and beyond?
5.3.37.Packaging trends: AiP goes commercial?
5.3.38.Packaging trends: from discreet bare die (COB) to wafer-level packaging and beyond?
5.3.39.Packaging trends: from discreet bare die (COB) to wafer-level packaging and beyond?
5.3.40.Comparison of die vs packaged options
5.3.41.eWLP vs flip chip and BGA in terms of insertion loss
5.3.42.Radar packaging: Material opportunities
5.3.43.Glass and panel level packaging of radars?
5.3.44.Function integration trend: from discreet to full chip-level function integration
5.3.45.Function integration trends: towards true radar-in-a-chip
5.3.46.Evolution of radar chips towards all-in-one designs
5.3.47.Evolution of radar chips: all-in-one designs
5.3.48.Board trends: from separate RF board to hybrid to full package integration?
5.3.49.Hybrid board is the norm
5.3.50.Hybrid board: what is it
5.4.Cameras
5.4.1.Camera technology: an overview of the market
5.4.2.Camera technology: CMOS is the bright spot in semiconductor sales landscape
5.4.3.How many camera needed in various levels of autonomy
5.4.4.CMOS image sensors vs CCD cameras
5.4.5.Key components in a CMOS image sensor (CIS)
5.4.6.Front vs backside illumination
5.4.7.Process flow for back-side-illuminated CMOS image sensors
5.4.8.Global vs Rolling Shutter
5.4.9.Global shutter: pixel size limitation and read-out mechanism
5.4.10.TPSCo: leading foundry for global shutter FSI CMOS on 65nm node
5.4.11.TPSCo: its best-in-class performance and partners
5.4.12.Sony: pixel architecture and performance of FSI global-shutter CMOS
5.4.13.Sony: back-side-illuminated stacked global shutter CMOS (breakthrough?)
5.4.14.Sony: BSI global shutter CMOS with stacked ADC
5.4.15.Omnivision: 2.2um GS CIS for automotive
5.4.16.Hybrid organic-Si global shutter CIS with high-res and low-noise
5.4.17.Hybrid QD-Si GS CIS at IR and NIR: achieving small pixels by physical separation of charge conversion and storage
5.4.18.Hybrid QD-Si GS CIS at IR and NIR: achieving small pixels by physical separation of charge conversion and storage
5.4.19.Hybrid QD-Si GS CIS at IR and NIR: achieving small pixels by physical separation of charge conversion and storage
5.4.20.Why one needs NIR sensing in machine vision
5.4.21.NIR sensing: limitation of Si CMOS
5.4.22.OmniVision: making silicon CMOS sensitive to NIR
5.4.23.OmniVision: making silicon CMOS sensitive to NIR
5.4.24.Deep trench isolation: innovation to reduce cross-talk
5.4.25.Deep trench isolation: innovation to reduce cross-talk
5.4.26.What is SWIR or short-wave-infra-red?
5.4.27.Why SWIR in autonomous mobility
5.4.28.Other SWIR benefits: better animal or on-road hazard detection
5.4.29.SWIR sensitivity of different materials (PbS QDs, Si, polymers, InGaAs, HgCdTe, etc)
5.4.30.SWIR: incumbent and emerging technology options
5.4.31.The challenge of high resolution, low cost IR sensors
5.4.32.Silicon based SWIR sensors: innovation
5.4.33.Silicon based SWIR sensors: innovation
5.4.34.Why colloidal quantum dots?
5.4.35.Quantum dots: choice of the material system
5.4.36.Advantage of solution processing: ease of integration with ROIC CMOS?
5.4.37.How is the QD layer applied?
5.4.38.Other ongoing challenges
5.4.39.Emberion: QD-graphene SWIR sensor
5.4.40.Emberion: QD-Graphene-Si broadrange SWIR sensor
5.4.41.SWIR Vision Sensors: first QD-Si cameras and/or an alternative to InVisage?
5.4.42.SWIR Vision Sensors: first QD-Si cameras and/or an alternative to InVisage?
5.4.43.SWIR Vision Sensors: first QD-Si cameras and/or an alternative to InVisage?
5.4.44.SWIR Vision Sensors: first QD-Si cameras and/or an alternative to InVisage?
5.4.45.QD-ROIC Si-CMOS integration examples (IMEC)
5.4.46.QD-ROIC Si-CMOS integration examples (RTI International)
5.4.47.QD-ROIC Si-CMOS integration examples (ICFO)
5.4.48.QD-ROIC Si-CMOS integration examples (ICFO)
5.5.AI software and computing platform
5.5.1.Terminologies explained: AI, machine learning, artificial neural networks, deep neural networks
5.5.2.Artificial intelligence: waves of development
5.5.3.Classical method: feature descriptors
5.5.4.Typical image detection deep neutral network
5.5.5.Algorithm training process in a single layer
5.5.6.Towards deep learning by deepening the neutral network
5.5.7.The main varieties of deep learning approaches explained
5.5.8.There is no single AI solution to autonomous driving
5.5.9.Application of AI to autonomous driving
5.5.10.End-to-end deep learning vs classical approach
5.5.11.Imitation learning for trajectory prediction: Valeo (1)
5.5.12.Imitation learning for trajectory prediction: Valeo (2)
5.5.13.Hybrid AI for Level 4/5 automation
5.5.14.Hybrid AI for object tracking
5.5.15.Hybrid AI for sensor fusion
5.5.16.Hybrid AI for motion planning
5.5.17.Autonomous driving requires different validation system
5.5.18.Validation of deep learning system?
5.5.19.The vulnerable road user challenge in city traffic
5.5.20.Multi-layered security needed for vehicle system
5.5.21.Aurora: building the full-stack AD solution
5.5.22.Argo AI: fully integrated AD driving system for OEMs
5.5.23.Drive.ai: AD retrofitting kit
5.5.24.Momenta: the Chinese AD solution provider
5.5.25.Sensor fusion for Mpilot Highway and Parking
5.5.26.HoloMatic: the Xuanyuan platform
5.5.27.The coming flood of data in autonomous vehicles
5.5.28.Computing power needed for autonomous driving
5.5.29.Horizon Robotics: the Chinese embedded AI chip unicorn
5.5.30.The paradigm shift of AI computing
5.5.31.Horizon Robotics: software and hardware roadmap
5.5.32.By-wire and AV domain computer
5.6.High-definition (HD) map
5.6.1.Lane models: uses and shortcomings
5.6.2.Localization: absolute vs relative
5.6.3.RTK systems: operation, performance and value chain
5.6.4.Sensors (GPS): price and market adoption (in unit numbers) evolution of GPS sensors
5.6.5.HD mapping assets: from ADAS map to full maps for level-5 autonomy
5.6.6.Many layers of an HD map for autonomous driving
5.6.7.HD map as a service
5.6.8.Who are the players?
5.6.9.Key business model differentiation between HD mapping players
5.6.10.Campines relying on vertical integration to build HD maps (TomTom. AutoNavi, Google, Here Technologies, etc)
5.6.11.Campines relying on camera to build HD maps (IvI5, Atlatec, Carmera, Mapper)
5.6.12.Campines relying on camera to build HD maps (IvI5, Atlatec, Carmera)
5.6.13.Companies building a map for specific firms: DeepMap
5.6.14.Enabling edge-level calculations
5.6.15.Semi- or fully automating the data-to-map process
5.6.16.Semi- or fully automating the data-to-map process
5.7.Teleoperation
5.7.1.Ottopia's advanced teleoperation for autonomous cars
5.7.2.Features of Ottopia's teleoperation platform
5.7.3.Business model of Ottopia
5.7.4.Phantom Auto's teleoperation as back-up for AVs
5.7.5.Phantom Auto gaining momentum in logistics
5.8.Cybersecurity
5.8.1.Cybersecurity risks for autonomous cars
5.8.2.Typical attack surfaces of a CAV
5.8.3.Vulnerable targets for hackers - connected ECUs
5.8.4.5StarS - consortium for cybersecurity assurance
5.8.5.Arilou's in-vehicle cybersecurity solutions
5.8.6.Argus's multi-layered cybersecurity solutions (1)
5.8.7.Argus's multi-layered cybersecurity solutions (2)
5.8.8.TowerSec's intrusion detection and prevention solution
5.8.9.C2A Security's in-vehicle cybersecurity protection
5.8.10.Regulus's cyber defense for GNSS sensors
5.9.5G and V2X
5.9.1.Why Vehicle-to-everything (V2X) is important for future autonomous vehicles
5.9.2.Two type of V2X technology: Wi-Fi vs cellular (1)
5.9.3.Regulatory: Wi-Fi based vs C-V2X
5.9.4.C-V2X assist the development of smart mobility
5.9.5.How C-V2X can support smart mobility
5.9.6.C-V2X includes two parts: via base station or direct communication
5.9.7.C-V2X via base station: vehicle to network (V2N)
5.9.8.5G technology enable direct communication for C-V2X
5.9.9.Architecture of C-V2X technology
5.9.10.Use cases and applications of C-V2X overview
5.9.11.C-V2X for automated driving use case (1)
5.9.12.C-V2X for automated driving use case (2)
5.9.13.Use cases of 5G NR C-V2X for autonomous driving
5.9.14.Other use cases
5.9.15.Case study: 5G to provide comprehensive view for autonomous driving
5.9.16.Case study: 5G to support HD content and driver assistance system
5.9.17.Timeline for the deployment of C-V2X
5.9.18.Progress so far
5.9.19.Landscape of supply chain
5.9.20.5G for autonomous vehicle: 5GAA
5.9.21.Ford C-V2X from 2022
6.MARKET FORECAST
6.1.Travel demand and mobility as a service (MaaS)
6.2.Travel demand and MaaS - summary (in trillion miles)
6.3.Passenger car sales will peak earlier than expected
6.4.Global passenger car sales forecast 2020-2040 - moderate (unit numbers)
6.5.Global passenger car sales forecast 2020-2040 - aggressive (unit numbers)
6.6.Global passenger car sales forecast 2020-2040 (thousand units) - summary
6.7.Global autonomous passenger car market forecast 2020-2040 (unit numbers)
6.8.Global private autonomous car market forecast 2020-2040 by level of autonomy (unit numbers)
6.9.Private-owned autonomous cars: a 20-year view
6.10.Global shared autonomous car market forecast 2020-2040 (unit numbers)
6.11.Shared autonomous cars: a 20-year view
6.12.Global autonomous passenger car market forecast 2020-2040 (unit numbers) - summary
6.13.Global autonomous car market forecast 2020-2040: AV sales and mobility service (market value)
6.14.Global autonomous car sales revenue forecast 2020-2040 by level of autonomy
6.15.Global autonomous car market forecast 2020-2040 (market value: $ million) - summary
6.16.Global autonomous car sales revenue breakdown 2020-2040 (market value)
6.17.Global autonomous car sales revenue breakdown 2020-2040 (market value) - summary
 

価格および注文方法

£$¥
Electronic (1-5 users)
£4,250.00
Electronic (6-10 users)
£6,250.00
Electronic and 1 Hardcopy (1-5 users)
£4,600.00
Electronic and 1 Hardcopy (6-10 users)
£6,575.00
Electronic (1-5 users)
€4,850.00
Electronic (6-10 users)
€7,000.00
Electronic and 1 Hardcopy (1-5 users)
€5,250.00
Electronic and 1 Hardcopy (6-10 users)
€7,450.00
Electronic (1-5 users)
$5,495.00
Electronic (6-10 users)
$7,995.00
Electronic and 1 Hardcopy (1-5 users)
$5,995.00
Electronic and 1 Hardcopy (6-10 users)
$8,495.00
Electronic (1-5 users)
¥598,000
Electronic (6-10 users)
¥868,000
Electronic and 1 Hardcopy (1-5 users)
¥648,000
Electronic and 1 Hardcopy (6-10 users)
¥928,000
Click here to enquire about additional licenses.
If you are a reseller/distributor please contact us before ordering.
お問合せ、見積および請求書が必要な方はm.murakoshi@idtechex.com までご連絡ください。

Subscription Enquiry